AdHoc enrollment process

ABSTRACT

Methods and systems are provided for an AdHoc enrollment process. A user may be able to enroll and be verified by a system for a variety of actions or authentications without being forced to turn over personally identifiable information and without having to formally enroll. The system may compare captured biometric information with existing biometric information and may identify the user without the use of personally identifiable information.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit of and priority, under 35U.S.C. § 119(e), to U.S. Provisional Application Ser. No. 62/982,351,filed on Feb. 27, 2020, entitled “ADHOC ENROLLMENT PROCESS,” the entiredisclosure of which is hereby incorporated by reference, in itsentirety, for all that it teaches and for all purposes.

FIELD

The present disclosure is generally directed to biometric authenticationand, in particular, toward an AdHoc enrollment process.

BACKGROUND

Security systems often require a method of authentication to permitaccess or otherwise allow entry to restricted areas or zones. One formof authentication may include identifying the individual seeking entryor access through the use of a personal identification number (PIN) orpassword. Other systems, such as biometric security systems, may usebiometrics for security purposes (e.g., fingerprint-recognitionsystems). The use of biometrics has significant advantages compared totraditional methods such as passwords and PINs. Often, biometric traitssuch as fingerprints, iris, and face scans are unique to the individual,non-invasive to acquire, and do not change with time. As such, biometrictraits are one of the best ways to connect an individual to a uniqueidentifier. Biometric traits utilize a unique human characteristic toverify the individual, rather than a user- or machine-generatedidentifier that can be compromised or forged. By using biometric traitsto identify an individual, the risk of incorrect identification islowered, while increasing the accuracy of a correct identification. Theuse of the unique human characteristic reduces the risk associated withlosing or forgetting other authentication forms (e.g., a PIN orpassword).

Currently, most security systems require a user to enroll in the systemto gain access to any restricted zone, area, or material. For example, asecurity system may need to have information unique to the individual(e.g., name, age, password, etc.) in order to identify the individualwhen the individual accesses the zone, area, or material guarded by thesecurity system. If the individual is not registered in the system, thesystem cannot recognize the individual, and may prevent access. Userswho wish to utilize or be recognized by the security system must divulgeinformation in order to be registered with the system, which the usermay wish to avoid.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a block diagram of a system in accordance with embodimentsof the present disclosure;

FIG. 2 shows a flowchart in accordance with embodiments of the presentdisclosure; and

FIG. 3 is a flowchart in accordance with embodiments of the presentdisclosure.

DETAILED DESCRIPTION

Embodiments of the present disclosure will be described in connectionwith an AdHoc enrollment process. Embodiments of the present disclosureat least beneficially enable a user to interact with a system withoutrequiring a formal enrollment process. The use of the AdHoc enrollmentprocess may, in some embodiments, improve or otherwise provide increasedprivacy and/or flexibility to an individual. For example, the individualmay wish to avoid disclosing excess or extraneous information whenattempting to verify his identity (e.g., providing a social securitynumber to access a secure area). The use of the AdHoc enrollment processmay allow the individual to simply provide a facial scan, which willthen be used later by a system (e.g., a company controlling access tothe secure area) to verify the user without requiring the user toprovide further information. In some embodiments, the individual may notneed provide further details, as a non-user device may be configured tocapture the individual's biometrics (e.g., a security camera capturing afacial scan) without the individual needing to separately submitinformation. Only information the individual wishes to divulge may beused by the system. Embodiments of the present disclosure further atleast beneficially enable identification of an individual based oncaptured biometric data. Exemplary embodiments of the present disclosurealso beneficially enable for identification of an individual based on apredetermined degree of likelihood, such as by permitting a user tomanually or automatically select a threshold for a confirmed matchbetween a captured scan and an existing scan.

Turning first to FIG. 1 , an exemplary system 100 in accordance with atleast one embodiment of the present disclosure is shown. The system 100as described herein permits a user to create and store biometric datafor use in, for example, identification of the user; to communicate(e.g., send and receive) information with other devices; to enroll auser (e.g., for whitelisting, verification, etc.); and/or to determineif the user is validated, authorized, and/or otherwise allowed to enterand/or access a location (e.g., enter a restricted area, determine ifthe user is whitelisted, determine if triggering an alarm is appropriategiven the identity of the user, etc.).

The system 100 comprises a computing device 102, a database 130, and/ora cloud or other network 134. Systems according to other embodiments ofthe present disclosure may comprise more or fewer components than thesystem 100. For example, the system 100 may not include the database130. In some embodiments, the database 130 may be located within thecomputing device 102.

The computing device 102 comprises at least one processor 104, at leastone user interface 108, at least one communication interface 112, atleast one extraction module 116, at least one memory 118, instructions120, one or more algorithms 124, one or more templates 128, and at leastone verification module 132. Computing devices according to otherembodiments of the present disclosure may comprise more or fewercomponents than the computing device 102.

The processor 104 of the computing device 102 may be any processordescribed herein or any similar processor(s). The processor 104 may beconfigured to execute instructions stored in the memory 118, whichinstructions may cause the processor 104 to carry out one or morecomputing steps utilizing or based on data received from the database130 and/or the cloud 134 as discussed at least in accordance with theflowcharts provided herein.

The memory 118 may be or comprise RAM, DRAM, SDRAM, other solid-statememory, any memory described herein, or any other tangible,non-transitory memory for storing computer-readable data and/orinstructions. The memory 118 may store information or data useful forcompleting, for example, any step of the method 200 and/or method 300described herein, or of any other methods. The memory 118 may store, forexample, instructions 120 and/or one or more algorithms 124. Suchinstructions or algorithms may, in some embodiments, be organized intoone or more applications, modules, packages, layers, or engines. Thealgorithms and/or instructions may cause the processor 104 to manipulatedata stored in the memory 118 and/or received from or via the database130 and/or the cloud 134.

The computing device 102 may also comprise one or more user interfaces108. The user interface 108 may be or comprise a keyboard, mouse,trackball, monitor, television, screen, touchscreen, smartphone, keypad,a physical security device, an electronic lock, and/or any other devicefor receiving information from a user and/or for providing informationto a user. The user interface 108 may be used, for example, to receive auser selection or other user input regarding any step of any methoddescribed herein. Notwithstanding the foregoing, any required input forany step of any method described herein may be generated automaticallyby the system 100 (e.g., by the processor 104 or another component ofthe system 100) or received by the system 100 from a source external tothe system 100. In some embodiments, the user interface 108 may beuseful to allow a user to modify instructions to be executed by theprocessor 104 according to one or more embodiments of the presentdisclosure, and/or to modify or adjust a setting of other informationdisplayed on the user interface 108 or corresponding thereto.

Although the user interface 108 is shown as part of the computing device102, in some embodiments, the computing device 102 may utilize a userinterface 108 that is housed separately from one or more remainingcomponents of the computing device 102. In some embodiments, the userinterface 108 may be located proximate one or more other components ofthe computing device 102, while in other embodiments, the user interface108 may be located remotely from one or more other components of thecomputer device 102.

The computing device 102 may also comprise a communication interface112. The communication interface 112 may be used for receiving imagedata or other information from an external source (the database 130, thecloud 134, and/or any other system or component not part of the system100), and/or for transmitting instructions or other information to anexternal system or device (e.g., another computing device 102, thedatabase 130, the cloud 134, and/or any other system or component notpart of the system 100). The communication interface 112 may compriseone or more wired interfaces (e.g., a USB port, an ethernet port, aFirewire port, coaxial cable, fiber-optic cable, and/or combinationsthereof) and/or one or more wireless transceivers or interfaces(configured, for example, to transmit and/or receive information via oneor more wireless communication protocols such as 802.11a/b/g/n/ac,Bluetooth®, NFC, ZigBee®, communication over a cloud network, 4G, 5G,antennas for transmitting/receiving wireless signals, combinationsthereof, and so forth). In some embodiments, the communication interface112 may be useful for enabling the computing device 102 to communicatewith one or more other processors 104 or computing devices 102, whetherto reduce the time needed to accomplish a computing-intensive task orfor any other reason.

The computing device 102 may also comprise an extraction module 116. Theextraction module may be configured to extract biometric informationassociated with a user. For instance, the user may wish to store orprovide one or more biometric items (e.g., fingerprints, iris scans,handprint scans, face scans, etc.), or the system 100 may require theuser to provide a biometric item (e.g., to access a restricted areamonitored by the system 100). In some embodiments, the extraction module116 may further access features or instructions in a user device tocapture the biometric information. Examples of devices in or used by theextraction module 116 include, but are not limited to, a fingerprintscanner, camera, microphone, combinations thereof, and/or any otherinternal or external capture method coupled with the user device tocapture the biometric information. In some embodiments, the extractionmodule 116 be manipulated by a processor (e.g., a processor 104) tocarry out the extraction of one or more biometric items in accordancewith embodiments of the present disclosure.

The computing device 102 may also comprise one or more templates 128.The template 128 may be recorded biometric information (e.g.,fingerprint scans, facial scans, iris scans, pulse rate measurements,palm scans, voice scans, blood pressure measurements, hand vein patternscans, ear scans, signature scans, etc.), biographical information(e.g., a password, a user PIN, a street address, a birth date, a phonenumber, a business name, etc.), behavioral data, metadata associatedwith the biometric information and/or biographical information, and/orany combinations thereof. In some embodiments, the template 128 may be acombination of one or more types of biometric information (e.g., acombination of data from a fingerprint scan and a face scan). Thetemplate 128 may be used by the system 100 and/or one or more componentsthereof (e.g., a computing device 102, a database 130, a cloud 134,etc.) to, for example, verify the identity of a user associated with thetemplate 128. In some embodiments, the template 128 may be compared toand/or matched with one or more existing templates (e.g., templatesstored in a database 130) to determine the identity of the user. Forinstance an algorithm, such as the algorithm 124, may compare thebiometric data contained in the template 128 (e.g., data associated withbiometric items captured or extracted from a user) with existingtemplates to identify the user (e.g., if the biometric data matches anexisting template, the system 100 may determine that the identity of theuser tied to the existing template is the identity of the user providingthe captured biometric data).

The computing device 102 may also comprise a verification module 132.The verification module 132 may be configured to pre-filter anyextracted information (e.g., biometric information extracted by theextraction module 116) to determine the authenticity of the capturedinformation. For instance, the verification module 132 may utilizepre-face filtering techniques to determine whether captured face scanstruly depict a the face of the user and can also have anti-spoofingtechnology to determine is the device is being subjected to apresentation attack. In instances where the captured scan is not of auser's face (e.g., the user was wearing a mask when the scan wasperformed), the verification module 132 may omit the storage and/or useof the captured scan or portions thereof. In another example, theverification module 132 may be or configured to connect to a scannerthat is capable of detecting silicon fingerprinting, such that theverification module 132 may be able to determine when a user isattempting to enter fingerprints of an individual who is not the user.In some embodiments, the verification module 132 may comprise a qualitymodule which may be capable of determining a quality associated with thecaptured biometric information. In such embodiments, the quality modulemay be instructions stored in a memory that, when executed by aprocessor, cause the processor to operate, for example, a machinelearning or artificial intelligence algorithm that may detect poorquality in the biometric information. For example, the algorithm may becapable of analyzing color values associated with pixels depicting acaptured facial scan. The color values may be compared to a predictedvalue, and an algorithm may determine a difference therebetween (e.g., adifference between each of the color values of each pixel and thepredicted value). The difference may then be compared to a predeterminedthreshold, with values exceeding the threshold being deemed insufficientfor use. In some embodiments, the algorithm may analyze sound waves(e.g., sound waves associated with a voice scan). In such embodiments,various aspects of the sound waves (e.g., wave amplitude, frequency,speech patterns, etc.) may be analyzed by the algorithm and compared topredicted sound wave values (e.g., human speech may be predicted to bewithin certain frequencies), which may be used to accept or reject thecaptured voice scan (e.g., a voice scan with too high or too low afrequency may be rejected).

In some embodiments, the verification module 132 may be configured tomatch, compare, or otherwise determine differences between two or morebiometric templates and/or one or more biometric items containedtherein. For instance, the verification module 132 may comprise or beprocessor (e.g., a processor 104) capable of utilizing one or morealgorithms (e.g., algorithms 124) to compare two biometric templates.The two biometric templates may include a biometric template createdbased on biometric information provided by a user (e.g., a facialcapture of a user with a camera or other imaging device) as well as abiometric template based on previously captured biometric information(e.g., a previously stored biometric template associated with the user).The algorithms utilized by the verification module may be artificialintelligence and/or machine learning programs configured to determinedifferences between the two biometric templates (e.g., visualdifferences, differences in stored data such as binary values, etc.) andmay generate a message (e.g., an electronic notification) based on theresults of the comparison.

For example, the algorithm may examine borders of the biometrictemplates (e.g., a shape of a border of a facial scan). The borders maybe examined based on, for example, shape (e.g., curvature and/or contourinformation), size (e.g., relative number of pixels or other valuescomposing the biometric template), combinations thereof, and/or thelike. In another example, the two biometric templates may be manipulatedor otherwise transformed (e.g., through linear transformations, linearmapping, passed through a hash function, etc.) and then compared. Thecomparison may comprise examining the behavior (e.g., changes in value,shape, density, etc.) of pixel values and/or stored data in the twotemplates after transforming the templates. In another example, thealgorithm may compare a biometric template comprising voice data with apreviously captured biometric template comprising voice data. Thealgorithm may examine different voice characteristics (e.g., speechpattern, pitch, tone, vocal frequency, sound wave amplitude, etc.) inthe data when comparing the two biometric templates.

The message may be a message (e.g., a confirmation or other electronicsignal) configured to indicate (e.g., to a system 100) whether the twobiometric templates match. In some embodiments, the message may be abinary signal that indicates whether or not a match has occurred. Forinstance, the message may confirm (e.g., as indicated by a value of 1)that a captured biometric template such as the template 128 matches(e.g., aligns with, shares similarities to a degree of confidence with,etc.) an existing template, and may alternatively deny (e.g., asindicated by a value of 0) that the captured biometric template does notmatch one or more existing templates. In some embodiments, the messagemay cause the system 100 to perform one or more actions or operations(e.g., permitting a user to access an area, whitelisting a user,triggering an alarm system, etc.) based on the results of the message.In some embodiments, the system 100, one or more components thereof,and/or systems external to the system 100 may idle or otherwise pausecertain operations until receipt of the message. For instance, inembodiments where the system 100 communicates with a system overseeingaccess to a restricted area, such as a security system overseeing entryto the restricted area through a locked door, the system 100 may firstextract (e.g., by a camera near the locked door) or otherwise requestthe user to submit a biometric item (e.g., a fingerprint scan). Thesystem 100 may then perform matching and send a message to the securitysystem indicating, based on the matching, whether the user is permittedto access the location. In such embodiments, the security system maykeep the individual from accessing the restricted area (e.g., by keepingthe door locked) until receipt of the message. Upon receipt, thesecurity system may perform one or more actions based on the message(e.g., causing the door to become unlocked in the event that the messageindicates that the user is authorized, causing the door to remain lockedin the event that the message indicates that the user is unauthorized,causing an alarm system to trigger, etc.).

FIG. 2 depicts a method 200 that may be used, for example, for an AdHocenrollment process. Generally speaking, the method is used to captureand add a user to a system without requiring the user formally enroll inthe system, while also maintaining user confidentiality by omittingassociating personally identifiable information (PII) therewith.

The method 200 (and/or one or more steps thereof) may be carried out orotherwise performed, for example, by at least one processor. The atleast one processor may be the same as or similar to the processor(s)104 of the computing device 102 described above. The at least oneprocessor may be part of a system (such as a system 100). A processorother than any processor described herein may also be used to executethe method 200. The at least one processor may perform the method 200 byexecuting instructions stored in a memory such as the memory 106. Theinstructions may correspond to one or more steps of the method 200described below. The instructions may cause the processor to execute oneor more algorithms, such as one or more algorithms 124.

The method 200 comprises capturing biometric information about a subject(e.g., a user) and adding a session tag without associating personallyidentifiable information (PII) with the subject (step 204). In someembodiments, the method 200 and/or one or more steps thereof such asstep 204 may be triggered based on one or more actions of a user. Forexample, the method 200 may begin when a user enters a restricted areaor attempts to do so. The user may approach or otherwise enter apredetermined distance of a locked door, for example, to access therestricted area. In such embodiments, the step 204 may avoid capturingthe biometric information from any individual in the area who is notattempting to enter the locked door (e.g., passing individuals who arenot within the predetermined distance of the locked door). In anotherexample, the method 200 may be used by a user to monitor entry ontoprivate property (e.g., an entry of an individual into a driveway on aprivate residence or property). The method 200 may capture a variety ofbiometric information from the user (e.g., a facial scan, a fingerprintscan, a voice scan, etc.). In some embodiments, the additional oralternative information (e.g., non-biometric information such as apassword) may be captured. In some embodiments, more than one biometricmay be captured (e.g., a facial scan and a voice scan) for use in themethod 200. For example, the user may be attempting to access a lockeddoor in the user's workplace and may permit the capture of a facial scanand a voice scan. A system (e.g., a system 100) may capture the facialscan using one or more extraction modules (e.g., a camera and amicrophone for the respective facial and voice scans) and may implementthe method 200 to process the extracted biometric information todetermine if the user may pass through the locked door (which isdescribed in greater detail below).

A session tag may be added to the captured biometric information. In oneembodiment, the session tag may depict information related to dataassociated with the capture biometric information (e.g., timestamp ofthe capture, label indicating type of biometric captured, etc.) withoututilizing or associating PII with the captured biometric information.For example, a security camera controlled by a company or organizationmay capture one or more biometric modalities associated with employeeswho are enrolled in the company. The company system may tagnon-employees (e.g., a janitor, a delivery man, other service providers,etc.) using non-PII labels (e.g., labeling the janitor with a meta-tagsuch as “JANITOR,” labeling a delivery provider as “DELIVERY,” etc.)with the label. The label may then be stored in a database (e.g., adatabase 130) without tagging the individual with PII.

In another example, a system (e.g., a system 100) may omit from labelingthe captured biometric information with personal user information, suchas a user's name, address, date of birth, social security number,physical characteristics (e.g., height, weight, etc.), driver's licenseinformation, bank account information, email address, combinationsthereof, and/or the like. The session tag may be an electronic label(e.g., metadata) that permits the system to recognize, categorize,and/or store the captured biometric information such that the biometricinformation can be accessed or retrieved from storage withoutsignificant effort. In some embodiments, the session tag may be used bythe system in lieu of other metadata to protect the identity and/orconfidentiality of an individual.

The method 200 also comprises extracting biometric data from thebiometric information (step 208). The extracted biometric data may beinformation organized or formatted in a predetermined formation (e.g.,sorted or otherwise stored as a matrix, vector, tensor, etc.) thatuniquely reflects the biometric information associated with the user.For example, in the case of a facial scan, the biometric data may be amatrix of values associated with the entropy, average energy, grayscalepixel value, combinations thereof, and/or the like of each pixel of thecaptured facial scan. The biometric data extracted may be stored by asystem (e.g., a system 100) and/or one or more components thereof (e.g.,a computing device 102, a database 130, etc.). In some embodiments, thebiometric data and/or the captured biometric information may betemporarily stored in the system, such that the data may be erased,removed, or deleted from the system after a predetermined time and/orstored in volatile memory (e.g., stored in RAM that is reset after thesystem is powered off).

In some embodiments, the amount of biometric data captured may be basedon the amount of certainty required to validate the user. For instance,in embodiments where access to a restricted area is required (e.g., abiohazardous laboratory, an R&D laboratory, a server room, etc.),additional biometric data may be requested by a system (e.g., a system100) to more accurately verify the user. In other embodiments, such asidentifying an authorized mailman to enter a property, the propertyowner may set the system to require less biometric data and/or fewerbiometric items (e.g., using only a facial scan to confirm the mailman).In some embodiments, a user (e.g., an authorized individual controllingthe system 100 and/or one or more components thereof) may have controlover setting a predetermined amount of biometric data required by thesystem depending on, for example, application of the system 100.

The method 200 also comprises creating a template from the extractedbiometric data (step 212). The template may be a rearrangement of theextracted biometric data (e.g., a linear transformation on the data)and/or a fusion (e.g., combination) of one or more biometric itemscontained in the biometric data. For example, the template may be amixture of biometric data associated with a facial scan and a voicescan, in embodiments where an individual has submitted both a facialscan and a voice scan to a system (e.g., a system 100 using one or moreextraction modules 116). The template may be stored, for example, in adatabase (e.g., a database 130) for later use by a system (e.g., asystem 100) and/or one or more components thereof (e.g., a computingdevice 102). In some embodiments, the template may be displayed on auser device (e.g., via a user interface 108) and/or may be communicated(e.g., using a communication interface 112) over a network.

The template may be used by one or more systems described herein and/orfor one or more of the methods described herein to verify the identityof the user. For instance, the template may be matched with an existingtemplate of the user (or otherwise compared, with a match being based onthe comparison surpassing or falling below a predetermined threshold).When the templates match, the system may confirm the identity of theuser. The system may generate, for example, a confirmation (e.g.,electronic signal) indicating that the templates match, and/orindicating the identity of the user.

The method 200 also comprises storing the template in a database (step216). The database may be any database mentioned herein (e.g., adatabase 130), but may additionally or alternatively be a database notspecifically mentioned herein. In some embodiments, the template mayonly be temporarily stored in the database (e.g., for a predeterminedamount of time) before being deleted, transferred, or otherwise removedfrom the database. The temporary storage may be used, for example, toprotect confidential user information, biometric information associatedwith the user, and/or the like. In some embodiments, the database may beconnected to one or more devices over a network and may be capable ofsharing the template and/or information related thereto to the one ormore devices. For example, the database may provide access for thirdparties to access the template when attempting to verify an individualor may otherwise use the template to identify an individual (e.g., suchas when a user attempts to access a platform, database, application,location, or the like).

The method 200 also comprises adding metadata to the database (step220). The added metadata may be data related to describing, labeling,and/or otherwise depicting information associated with the template(e.g., a set of fields describing information in addition to theinformation stored in the template). The metadata may include, forexample, a timestamp (e.g., a date, time, and/or location at which thetemplate was created), size information (e.g., the amount of spaceneeded to store the template), user information (e.g., name, date ofbirth, etc.), file type, combinations thereof, and/or the like. In someembodiments, the metadata may exclude or omit metadata directed to PII(e.g., the metadata may not use data such as name or date of birth toavoid tying or otherwise attributing the template to a specific user).The metadata may be automatically applied by a system (e.g., a system100) and/or components thereof (e.g., a computing device 102). Forexample, in embodiments where a facial scan is a captured by a camera,the camera may send metadata information (e.g., date and time of thecaptured image) to the database 130. A processor (e.g., a processor 104)may receive the metadata information and connect the information to thebiometric capture and/or the template.

The method 200 also comprises comparing the biometric template to one ormore existing biometric templates (step 224). A processor (e.g., aprocessor 104) may make use of an algorithm (e.g., an algorithm 124) tocompare the biometric template to one or more existing templates. Insome embodiments, the algorithm may determine differences between thecompared templates (e.g., based on pixel values, matrix values, matrixsizes/dimensions, etc.). In some embodiments, the comparison may makeuse of a threshold to verify or determine that the biometric templatematches an existing template. For example, the algorithm may provide oroutput a quantified degree of similarity (or difference) between thebiometric template and each of the existing biometric templates (e.g., apercentage of matching values of pixels or matrices between thebiometric template and each of the existing biometric templates, astatistical likelihood that the compared values depict the sametemplate, etc.).

In such embodiments, the degree of similarity may be based on the amountof biometric data captured. For instance, if a user of the system 100desires fewer captured biometric items (e.g., when using the system topermit a mailman to access a property), the user may lower the thresholdneeded to return a positive result. The lower threshold may allow themailman to be passively identified (e.g., a facial scan is captured by acamera on the property without the mailman needing to stop and submitthe facial scan), saving time and providing peace of mind to the user.In other embodiments, such as when accessing a restricted area, a userof the system may require additional biometric data (e.g., a facialscan, a fingerprint scan, and a voice scan) to verify the individualattempting to access the restricted area. The user may additionally oralternatively require a higher threshold of accuracy to increase thelikelihood that only authorized individuals access the restricted area.

The method 200 also comprises generating a message based on thecomparison (step 228). The message (e.g., confirmation signal,electronic signal, etc.) may indicate a result of the comparison. Forinstance, when the comparison of the biometric template to the one ormore existing biometric templates, such as in the step 224, indicatesthat the two templates match (or meet a quantified degree ofsimilarity), the message may indicate that the biometric template andthe existing biometric template represent the same data (e.g., thetemplates belong to or otherwise represent the same person). The messagemay be communicated to one or more devices over a network. In oneembodiment, the message may be used to compare the user with a whitelistor to trigger an alarm. For example, in embodiments using a whitelistthe message may indicate that the person matches an individual on thewhitelist. A system (e.g., a system 100) may then permit the person toenter a restricted area (e.g., by unlocking a door to an area that isoff limits to individuals not on the whitelist). In some embodiments,the message may trigger an alarm system. For instance, a security systemmay receive the message indicating that the person is in an unauthorizedlocation in a building and may notify security and/or cause an alarm tobe triggered.

The present disclosure encompasses embodiments of the method 200 thatcomprise more or fewer steps than those described above, and/or one ormore steps that are different than the steps described above.

A method 300 comprises receiving biometric data (step 304). Thebiometric data may be data extracted from a biometric capture, such as afacial scan, fingerprint scan, an iris scan, combinations thereof,and/or the like. In some embodiments, the receive biometric data may bethe biometric information captured (e.g., by a step 204) by one or moredevices (e.g., a camera). The biometric data may be received from asystem (e.g., a system 100) and/or one or more components thereof (e.g.,a database 130, a computing device 102, etc.).

The method 300 also comprises classifying the received biometric data(step 308). The method 300 may be carried out by, for example, aprocessor (e.g., a processor 104) accessing one or more algorithms(e.g., an algorithm 124) to classify the received biometric data (e.g.,using a support vector machine trained on similar biometric data toclassify the received biometric data, using a K-Nearest Neighbor (KNN)algorithm, etc.). The classifying may comprise comparing one or morefeatures of the received biometric data to existing biometric data toappropriately classify the biometric data. For instance, in embodimentswhere the biometric data is related to a facial scan, a system (e.g., asystem 100) and/or one or more components thereof (e.g., a processor104), may use a face classifier (e.g., a machine learning algorithmtrained on facial data) to verify that the biometric data pertains to aface and/or to output a confidence value (e.g., a match score)associated with the facial data. The confidence value may indicate adegree of likelihood that the data passing through the face classifieris indeed data associated with a face. The confidence value may bepercent based (e.g., 99%, 95%, 90%, etc.), which may represent thelikelihood that the classified biometric data pertains to the biometricitem used to train the system. In some embodiments, the classifying maycomprise using more than one classifier to classify the biometric data.For instance, the biometric data may correspond to a facial scan, andthe biometric data may be passed into one or more classifiers trained onvarious data (e.g., a classifier trained on fingerprint scan data, aclassifier trained on facial scan data, etc.). The one or moreclassifiers may then output confidence values. In this embodiment, sincethe biometric data corresponds to a facial scan, the confidence valueassociated with passing the biometric data through a classifier trainedon facial scan data may be a higher confidence (e.g., be a higherconfidence value) than the confidence value associated with passing thebiometric data through a classifier trained on fingerprint scan data,which may indicate that the biometric data may be more likely to bebased on a facial scan than a fingerprint scan. A system (e.g., thesystem 100) and/or components thereof (e.g., a computing device 102) mayreceive the confidence values and determine, based on the best (e.g.,highest, closest, etc.) score, what type of biometric capture thebiometric data represents, and label the biometric data accordingly(e.g., using metadata to indicate what type of biometric capture isrepresented by the biometric data).

The method 300 also comprises selecting an appropriate Support VectorMachine (SVM) based on the classified biometric data (step 312). Theselected SVM may be based on, for example, the biometric data receivedin the method 300. The SVM may be used by a system (e.g., a system 100)and/or one or more components thereof (e.g., a computing device 102) tofuse biometric data into a unique template matrix. In some embodiments,the biometric data may be data related to more than one biometric item(e.g., a facial scan and a fingerprint scan). This may occur, forexample, when a user inputs more than one biometric scan (e.g., a facialscan and a voice scan) for verification. In such embodiments,classifiers trained on the respective data may be used in combination toclassify the data. For instance, a classifier may be trained using bothfacial scan data and voice scan data (e.g., a face and voice scan SVM),and may be used by the system and/or one or more components thereof toprocess the data to create a unique template matrix (e.g., to fuseclassifications).

The method 300 also comprises fusing biometric data into a template(step 316). The fusing may include using an SVM (e.g., a face andfingerprint SVM configured to combine data associated with a facial scanand data associated with a fingerprint scan). The template may be afusion (e.g., combination, mix, etc.) of the biometric data. In someembodiments, the selected SVM may manipulate (e.g., using matrixmultiplication, linear mapping, etc.) the biometric data into variouslayers to form a template. For example, the biometric data may compriseface and fingerprint data. The selected SVM (e.g., a face andfingerprint SVM configured to receive face and fingerprint data) mayreceive the biometric data and output a manipulated version of the data(e.g., using linear mapping) as a template. In some embodiments, the SVMmay take or make multiple transformations to the biometric data tofurther obscure or mix the biometric data. The further mixing mayprevent or increase the difficulty associated with recovering theoriginal biometric data, which further protects individual data frompotential bad actors (e.g., hackers, scammers, etc.). The fused templatemay be stored in the system and/or one or more components thereof (e.g.,a database 130).

In some embodiments, the fusion (e.g., of face data and fingerprintdata) may be given by the Neyman-Pearson lemma, using distributions ofgenuine and imposter fusion score distributions. A fusion formulaapproximating the fusion using a false match rate (FMR) based onimposter scores may be derived based on imposter scores. The probabilityof falsely matching all n biometrics is the product of probabilities ofa false match, given that the biometrics are independent of one another.In some embodiments, the scores may be defined logarithmically as afunction of the FMR determined from a general test set of imposters. Thelogarithmic mapping changes the product to a sum of probabilities, whichmay then be adjusted using a function to approximate the Neyman-Pearsonfusion.

By defining the scores logarithmically as a function of the FMR andcombining the sum of probabilities, genuine mated pairs are not neededto model scores. The adjustment using the function ensures the outputscores are defined logarithmically as a function of FMR. This allows asystem to define a single threshold for the fusion of multiple biometricmodalities, which is directly related to system performance. The methodmay be applicable to both homogenous fusion (e.g., multipleinstantiations of a single biometric modality) and heterogenous fusion(e.g., multiple biometric modalities).

Examples of techniques that disclose how to perform fusion (e.g., usingscore distributions) that can be used with any one or more of theembodiments disclosed herein are:

-   1. J. P. Hube. Neyman-Pearson Biometric Score Fusion as an Extension    of the Sum Rule, SPIE Biometric Technology for Human Identification    IV, Orlando, Fla., 2007.-   2. J. P. Hube. Formulae for consistent biometric score level fusion,    2017 IEEE International Joint Conference on Biometrics (IJCB),    Denver, Colo., 2017, pp. 329-334.

Both of which are incorporated herein by reference in their entirety.

The method 300 also comprises matching the template with an existingtemplate (step 320). The matching in the step 320 of the method 300 maybe similar to or the same as the step 224 of the method 200. Forexample, the method 300 may use one or more algorithms (e.g., analgorithm 124) to determine a match between the template and theexisting template. In some embodiments, the algorithm may compare thetemplate to one or more existing templates to determine the closestmatch. In some embodiments, if the algorithm does not match the templateto an existing template, a processor (e.g., a processor 104) maydetermine that the template corresponds to an unknown or unidentifiedperson (e.g., a user without a previously constructed template). Thecomparison may include comparing, for example, pixel values, biometricdata patterns, matrix values, combinations thereof, and/or the like. Forinstance, a template comprising face and voice data may be compared toexisting templates containing both face and voice data.

In some embodiments, the matching may comprise using unimodal matching.Unimodal matching may include breaking down the template to a singlesample, instance, or feature. For instance, the template may be comparedagainst existing single samples of facial scans (e.g., scans of front,right, and/or left profiles), fingerprint scans (e.g., thumb scan,forefinger scan, middle finger scan, combinations thereof, etc.), sensorscans (e.g., IR scan of individual face, depth sensors, RGB images,etc.), unique biometric features (e.g., a fingerprint scan of a fingerwith tissue damage), iris scans, combinations thereof, and/or the like.The algorithm may determine an optimized unimodal output to determinethe most closely matching existing template. After deconstructing thetemplate to one or more of the single samples, the algorithm may outputthe most closely matching (e.g., a degree of similarity exceeding orfalling below (depending on application) a threshold value) template. Inone example, the template may include data corresponding to an iris scanand a facial scan. The algorithm may compare the template using unimodalmatching, and which may return a degree of similarity or confidencevalue associated with the comparison. In this example, any existingtemplates that contain similar iris scan and facial scan data may bereturned as a match.

Additionally or alternatively, the matching may comprise usingmultimodal matching. Multimodal matching may include comparing thebiometric data to multiple traits or scans. For example, the biometrictemplate may be deconstructed into iris scans and fingerprint scans. Thedeconstructed biometric data may be compared with existing iris scansand fingerprint scans, with the closest matches being returned by thealgorithm. In some embodiments, both unimodal and multimodal matchingmay be implemented and compared to select existing biometric data.

The method 300 also comprises generating a confirmation when thebiometric template matches the existing template (step 324). Theconfirmation may be based on the results of the comparison of thebiometric template to existing templates. For instance, if the matchingdetermines that a biometric template (e.g., containing facial scan data)matches an existing template (e.g., facial scan data previously captureor stored), a confirmation that the template matches the existingtemplate, which may indicate that the facial scan provided by theindividual matches an existing facial scan. In some embodiments, themethod 300 may utilize a threshold for generating the confirmation. Thethreshold may be defined by the system, components thereof, and/or auser and may indicate that a degree of match between the generatedtemplate and the existing templates sufficient to permit certainactions, such as identifying the user as a member of a whitelist. Theconfirmation may be sent to one or more components of a system (e.g., asystem 100), which may determine whether or not to perform an action(e.g., indicate that the user is on a whitelist).

In some embodiments, the system may define a percent threshold (e.g.,99.9%, 99%, 98%, 97%, etc.) above which the system defines the biometrictemplate as matching the existing template. For example, a threshold of98% may indicate that, when the output of an SVM outputs a confidencevalue or degree of similarity at or above 98%, the two templates will betreated as the same with respect to the biometric data received by theSVM and a confirmation (e.g., an electronic signal) will be generated.In this example, any value falling below the 98% threshold will notreturn a confirmation, which may indicate that the biometric templatedoes not match the existing template, at least to a desired degree ofcertainty (e.g., there is insufficient certainty that, if the systemperforms an action, such as indicating the user is on a whitelist, theuser would actually be on the whitelist).

The method 300 also comprises generating an alert when the biometrictemplate does not match the existing template (step 328). The alert maybe based on the results of the comparison of the biometric template toexisting templates. For example, if the comparison indicates that thebiometric template does not match any of the existing templates (e.g., apredetermined threshold for identifying a match has not been met), thebiometric template and the existing templates may not match. The alertmay be sent to a system (e.g., a system 100) and/or one or morecomponents thereof (e.g., a computing device 102) to indicate that thebiometric template does not match any of the existing templates. Thealert may indicate to the system, components thereof, a user, and/or thelike that the user cannot be identified by the system. The alert may bean audible alert, a visual alert, or a combination thereof.

The present disclosure encompasses embodiments of the method 300 thatcomprise more or fewer steps than those described above, and/or one ormore steps that are different than the steps described above.

Any of the steps, functions, and operations discussed herein can beperformed continuously and automatically.

The exemplary systems and methods of this disclosure have been describedin relation to an AdHoc enrollment process. However, to avoidunnecessarily obscuring the present disclosure, the precedingdescription omits a number of known structures and devices. Thisomission is not to be construed as a limitation of the scope of theclaimed disclosure. Specific details are set forth to provide anunderstanding of the present disclosure. It should, however, beappreciated that the present disclosure may be practiced in a variety ofways beyond the specific detail set forth herein.

Furthermore, while the exemplary embodiments illustrated herein show thevarious components of the system collocated, certain components of thesystem can be located remotely, at distant portions of a distributednetwork, such as a LAN and/or the Internet, or within a dedicatedsystem. Thus, it should be appreciated, that the components of thesystem can be combined into one or more devices, such as a server,communication device, or collocated on a particular node of adistributed network, such as an analog and/or digital telecommunicationsnetwork, a packet-switched network, or a circuit-switched network. Itwill be appreciated from the preceding description, and for reasons ofcomputational efficiency, that the components of the system can bearranged at any location within a distributed network of componentswithout affecting the operation of the system.

Furthermore, it should be appreciated that the various links connectingthe elements can be wired or wireless links, or any combination thereof,or any other known or later developed element(s) that is capable ofsupplying and/or communicating data to and from the connected elements.These wired or wireless links can also be secure links and may becapable of communicating encrypted information. Transmission media usedas links, for example, can be any suitable carrier for electricalsignals, including coaxial cables, copper wire, and fiber optics, andmay take the form of acoustic or light waves, such as those generatedduring radio-wave and infra-red data communications.

While the flowcharts have been discussed and illustrated in relation toa particular sequence of events, it should be appreciated that changes,additions, and omissions to this sequence can occur without materiallyaffecting the operation of the disclosed embodiments, configuration, andaspects.

A number of variations and modifications of the disclosure can be used.It would be possible to provide for some features of the disclosurewithout providing others.

In yet another embodiment, the systems and methods of this disclosurecan be implemented in conjunction with a special purpose computer, aprogrammed microprocessor or microcontroller and peripheral integratedcircuit element(s), an ASIC or other integrated circuit, a digitalsignal processor, a hard-wired electronic or logic circuit such asdiscrete element circuit, a programmable logic device or gate array suchas PLD, PLA, FPGA, PAL, special purpose computer, any comparable means,or the like. In general, any device(s) or means capable of implementingthe methodology illustrated herein can be used to implement the variousaspects of this disclosure. Exemplary hardware that can be used for thepresent disclosure includes computers, handheld devices, telephones(e.g., cellular, Internet enabled, digital, analog, hybrids, andothers), and other hardware known in the art. Some of these devicesinclude processors (e.g., a single or multiple microprocessors), memory,nonvolatile storage, input devices, and output devices. Furthermore,alternative software implementations including, but not limited to,distributed processing or component/object distributed processing,parallel processing, or virtual machine processing can also beconstructed to implement the methods described herein.

In yet another embodiment, the disclosed methods may be readilyimplemented in conjunction with software using object or object-orientedsoftware development environments that provide portable source code thatcan be used on a variety of computer or workstation platforms.Alternatively, the disclosed system may be implemented partially orfully in hardware using standard logic circuits or VLSI design. Whethersoftware or hardware is used to implement the systems in accordance withthis disclosure is dependent on the speed and/or efficiency requirementsof the system, the particular function, and the particular software orhardware systems or microprocessor or microcomputer systems beingutilized.

In yet another embodiment, the disclosed methods may be partiallyimplemented in software that can be stored on a storage medium, executedon programmed general-purpose computer with the cooperation of acontroller and memory, a special purpose computer, a microprocessor, orthe like. In these instances, the systems and methods of this disclosurecan be implemented as a program embedded on a personal computer such asan applet, JAVA® or CGI script, as a resource residing on a server orcomputer workstation, as a routine embedded in a dedicated measurementsystem, system component, or the like. The system can also beimplemented by physically incorporating the system and/or method into asoftware and/or hardware system.

Although the present disclosure describes components and functionsimplemented in the embodiments with reference to particular standardsand protocols, the disclosure is not limited to such standards andprotocols. Other similar standards and protocols not mentioned hereinare in existence and are considered to be included in the presentdisclosure. Moreover, the standards and protocols mentioned herein andother similar standards and protocols not mentioned herein areperiodically superseded by faster or more effective equivalents havingessentially the same functions. Such replacement standards and protocolshaving the same functions are considered equivalents included in thepresent disclosure.

The present disclosure, in various embodiments, configurations, andaspects, includes components, methods, processes, systems and/orapparatus substantially as depicted and described herein, includingvarious embodiments, subcombinations, and subsets thereof. Those ofskill in the art will understand how to make and use the systems andmethods disclosed herein after understanding the present disclosure. Thepresent disclosure, in various embodiments, configurations, and aspects,includes providing devices and processes in the absence of items notdepicted and/or described herein or in various embodiments,configurations, or aspects hereof, including in the absence of suchitems as may have been used in previous devices or processes, e.g., forimproving performance, achieving ease, and/or reducing cost ofimplementation.

The foregoing discussion of the disclosure has been presented forpurposes of illustration and description. The foregoing is not intendedto limit the disclosure to the form or forms disclosed herein. In theforegoing Detailed Description for example, various features of thedisclosure are grouped together in one or more embodiments,configurations, or aspects for the purpose of streamlining thedisclosure. The features of the embodiments, configurations, or aspectsof the disclosure may be combined in alternate embodiments,configurations, or aspects other than those discussed above. This methodof disclosure is not to be interpreted as reflecting an intention thatthe claimed disclosure requires more features than are expressly recitedin each claim. Rather, as the following claims reflect, inventiveaspects lie in less than all features of a single foregoing disclosedembodiment, configuration, or aspect. Thus, the following claims arehereby incorporated into this Detailed Description, with each claimstanding on its own as a separate preferred embodiment of thedisclosure.

Moreover, though the description of the disclosure has includeddescription of one or more embodiments, configurations, or aspects andcertain variations and modifications, other variations, combinations,and modifications are within the scope of the disclosure, e.g., as maybe within the skill and knowledge of those in the art, afterunderstanding the present disclosure. It is intended to obtain rights,which include alternative embodiments, configurations, or aspects to theextent permitted, including alternate, interchangeable and/or equivalentstructures, functions, ranges, or steps to those claimed, whether or notsuch alternate, interchangeable and/or equivalent structures, functions,ranges, or steps are disclosed herein, and without intending to publiclydedicate any patentable subject matter.

Aspects of the disclosed technology are directed to:

Capturing biometric information associated with a user; extractingbiometric data from the biometric information; generating a firsttemplate representative of the biometric data; comparing the firsttemplate with one or more stored templates to quantify a degree ofsimilarity therebetween; sending, when the quantified degree ofsimilarity between first template and a second template exceed athreshold, a confirmation.

Any of the above aspects, wherein the confirmation is sent over anetwork to a user device.

Any of the above aspects, wherein the biometric data comprisesinformation captured from one or more of a facial scanner, a camera, amicrophone, a fingerprint scanner, and an iris scanner.

Any of the above aspects, wherein the confirmation triggers an alarmsystem.

Any of the above aspects, wherein the confirmation indicates the user ison a whitelist.

Any of the above aspects, wherein the comparing the first template withthe one or more stored templates comprises using an algorithm toclassify the first template.

Any of the above aspects, wherein the algorithm is a Support VectorMachine (SVM).

Any of the above aspects, wherein the algorithm is a K-Nearest Neighbor(KNN) algorithm.

Any of the above aspects, wherein the threshold is set by a user device.

A system comprising: a processor; and a memory storing instructionsthereon that, when executed by the processor, cause the processor to:receive biometric information associated with a first user; extractbiometric data from the biometric information; generate a first templaterepresentative of the biometric data; compare the first template withone or more stored templates to quantify a degree of similaritytherebetween; and sending, when the quantified degree of similaritybetween the first template and the second template is below a threshold,an alert.

Any of the above aspects, wherein the biometric information isinformation related to one or more of a facial scan, an iris scan, afingerprint scan, and a voice scan.

Any of the above aspects, wherein the alert is sent over a communicationnetwork to a user device.

Any of the above aspects, wherein the alert triggers an alarm system.

Any of the above aspects, wherein the first template comprises biometricinformation related to one or more of a facial scan, an iris scan, afingerprint scan, and a voice scan.

Any of the above aspects, wherein the degree of similarity is set by asecond user different from the first user.

Any of the above aspects, further comprising: labeling, with a sessiontag, the biometric information.

Any of the above aspects, wherein the session tag omits associatingpersonally identifiable information (PII) with the first user.

Any of the above aspects, wherein a record of the session tag is storedin a database.

A system comprising: a processor; an imaging device; a user devicecoupled to the processor; and a memory storing instructions thereonthat, when executed by the processor, cause the processor to: capture,using the imaging device, an image of a user; add a session tag to thecaptured image; generate a first template from the captured image;determine, based on a predetermined threshold, whether the firsttemplate matches a second template; when the first template matches thesecond template, generate and send a confirmation to the user device;and when the first template does not match the second template, generateand send an alert the user device.

Any of the above aspects, wherein the user device is configured to atleast one of decrease and increase the predetermined threshold.

A non-transitory computer-readable medium comprising a set ofinstructions stored therein which, when executed by a processor, causethe processor to: capture biometric information associated with a user;extract biometric data from the biometric information; generate a firsttemplate representative of the biometric data; compare the firsttemplate with one or more stored templates to quantify a degree ofsimilarity therebetween; and send, when the quantified degree ofsimilarity between the first template and a second template exceed athreshold, a confirmation.

Any of the above aspects, wherein the biometric information isinformation related to one or more of a facial scan, an iris scan, afingerprint scan, and a voice scan.

Any of the above aspects, wherein the alert is sent over a communicationnetwork to a user device.

Any of the above aspects, wherein the alert triggers an alarm system.

Any of the above aspects, wherein the first template comprises biometricinformation related to one or more of a facial scan, an iris scan, afingerprint scan, and a voice scan.

Any of the above aspects, wherein the degree of similarity is set by asecond user different from the first user.

Any of the above aspects, further comprising: labeling, with a sessiontag, the biometric information.

Any of the above aspects, wherein the session tag omits associatingpersonally identifiable information (PII) with the first user.

Any of the above aspects, wherein a record of the session tag is storedin a database.

A system comprising: a means for capturing biometric informationassociated with a user; a means for extracting biometric data from thebiometric information; a means for generating a first templaterepresentative of the biometric data; a means for comparing the firsttemplate with one or more stored templates to quantify a degree ofsimilarity therebetween; and a means for sending, when the quantifieddegree of similarity between the first template and a second templateexceed a threshold, a confirmation.

Any of the above aspects, wherein the biometric information isinformation related to one or more of a facial scan, an iris scan, afingerprint scan, and a voice scan.

Any of the above aspects, wherein the alert is sent over a communicationnetwork to a user device.

Any of the above aspects, wherein the alert triggers an alarm system.

Any of the above aspects, wherein the first template comprises biometricinformation related to one or more of a facial scan, an iris scan, afingerprint scan, and a voice scan.

Any of the above aspects, wherein the degree of similarity is set by asecond user different from the first user.

Any of the above aspects, further comprising: a means for labeling, witha session tag, the biometric information.

Any of the above aspects, wherein the session tag omits associatingpersonally identifiable information (PII) with the first user.

Any of the above aspects, wherein a record of the session tag is storedin a database.

A method comprising: registering a user biometric, comprising: capturinga first biometric modality associated with a user at a first time;extracting first biometric data from the biometric modality; andgenerating a first template based on the first biometric data; and

enrolling the user in a system, comprising: capturing the firstbiometric modality associated with the user at a second time after thefirst time; extracting second biometric data from the first biometricmodality; generating a second template based on the second biometricdata; comparing the second template with the first template to quantifya degree of similarity therebetween; and sending, when the quantifieddegree of similarity between the first template and a second templateexceed a threshold, a confirmation.

Any of the above aspects, wherein the confirmation is sent over anetwork to a user device.

Any of the above aspects, wherein the first biometric data and thesecond biometric data comprise information captured from one or more ofa facial scanner, a camera, a microphone, a fingerprint scanner, and aniris scanner.

Any of the above aspects, wherein the confirmation triggers an alarmsystem.

Any of the above aspects, wherein the confirmation indicates the user ison a whitelist.

Any of the above aspects, wherein the comparing the first template withthe second template comprises using an algorithm to classify the firsttemplate.

Any of the above aspects, wherein the algorithm is a Support VectorMachine (SVM).

Any of the above aspects, wherein the algorithm is a K-Nearest Neighbor(KNN) algorithm.

Any of the above aspects, wherein the threshold is set by a user device.

A system comprising: a processor; and a memory storing instructionsthereon that, when executed by the processor, cause the processor to:receive a first biometric modality and a second biometric modalityassociated with a user; generate a first template, the first templatebeing a fusion of data associated with the first biometric modality andthe second biometric modality; compare the first template with one ormore stored templates to quantify a degree of similarity therebetween;and send, when the quantified degree of similarity between the firsttemplate and each of the one or more stored templates is below athreshold, an alert.

Any of the above aspects, wherein the first biometric modality and thesecond biometric modality each comprise information related to one ormore of a facial scan, an iris scan, a fingerprint scan, and a voicescan.

Any of the above aspects, wherein the alert is sent over a communicationnetwork to a user device.

Any of the above aspects, wherein the alert triggers an alarm system.

Any of the above aspects, wherein the first template is determined usingthe Neyman-Pearson lemma.

Any of the above aspects, wherein the degree of similarity is apercent-based value.

Any of the above aspects, further comprising: labeling, with arespective session tag, each of the first biometric modality and thesecond biometric modality.

Any of the above aspects, wherein each of the session tags omitsassociating personally identifiable information (PII) with either of thefirst biometric modality and the second biometric modality.

Any of the above aspects, wherein a record of each of the session tagsis stored in a database.

Any aspect in combination with any one or more other aspects.

Any one or more of the features disclosed herein.

Any one or more of the features as substantially disclosed herein.

Any one or more of the features as substantially disclosed herein incombination with any one or more other features as substantiallydisclosed herein.

Any one of the aspects/features/embodiments in combination with any oneor more other aspects/features/embodiments.

Use of any one or more of the aspects or features as disclosed herein.

The phrases “at least one,” “one or more,” “or,” and “and/or” areopen-ended expressions that are both conjunctive and disjunctive inoperation. For example, each of the expressions “at least one of A, Band C,” “at least one of A, B, or C,” “one or more of A, B, and C,” “oneor more of A, B, or C,” “A, B, and/or C,” and “A, B, or C” means Aalone, B alone, C alone, A and B together, A and C together, B and Ctogether, or A, B and C together.

The term “a” or “an” entity refers to one or more of that entity. Assuch, the terms “a” (or “an”), “one or more,” and “at least one” can beused interchangeably herein. It is also to be noted that the terms“comprising,” “including,” and “having” can be used interchangeably.

The term “automatic” and variations thereof, as used herein, refers toany process or operation, which is typically continuous orsemi-continuous, done without material human input when the process oroperation is performed. However, a process or operation can beautomatic, even though performance of the process or operation usesmaterial or immaterial human input, if the input is received beforeperformance of the process or operation. Human input is deemed to bematerial if such input influences how the process or operation will beperformed. Human input that consents to the performance of the processor operation is not deemed to be “material.”

Aspects of the present disclosure may take the form of an embodimentthat is entirely hardware, an embodiment that is entirely software(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module,” or “system.”Any combination of one or more computer-readable medium(s) may beutilized. The computer-readable medium may be a computer-readable signalmedium or a computer-readable storage medium.

A computer-readable storage medium may be, for example, but not limitedto, an electronic, magnetic, optical, electromagnetic, infrared, orsemiconductor system, apparatus, or device, or any suitable combinationof the foregoing. More specific examples (a non-exhaustive list) of thecomputer-readable storage medium would include the following: anelectrical connection having one or more wires, a portable computerdiskette, a hard disk, a random access memory (RAM), a read-only memory(ROM), an erasable programmable read-only memory (EPROM or Flashmemory), an optical fiber, a portable compact disc read-only memory(CD-ROM), an optical storage device, a magnetic storage device, or anysuitable combination of the foregoing. In the context of this document,a computer-readable storage medium may be any tangible medium that cancontain or store a program for use by or in connection with aninstruction execution system, apparatus, or device.

A computer-readable signal medium may include a propagated data signalwith computer-readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer-readable signal medium may be any computer-readable medium thatis not a computer-readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device. Program codeembodied on a computer-readable medium may be transmitted using anyappropriate medium, including, but not limited to, wireless, wireline,optical fiber cable, RF, etc., or any suitable combination of theforegoing.

The terms “determine,” “calculate,” “compute,” and variations thereof,as used herein, are used interchangeably and include any type ofmethodology, process, mathematical operation or technique.

What is claimed is:
 1. A method comprising: registering a userbiometric, comprising: capturing a first biometric modality associatedwith a user at a first time; extracting first biometric data from thebiometric modality; and generating a first template based on the firstbiometric data; and enrolling the user in a system, comprising:capturing the first biometric modality associated with the user at asecond time after the first time; extracting second biometric data fromthe first biometric modality; generating a second template based on thesecond biometric data; comparing the second template with the firsttemplate to quantify a degree of similarity therebetween; and sending,when the quantified degree of similarity between the first template anda second template exceed a threshold, a confirmation.
 2. The method ofclaim 1, wherein the confirmation is sent over a network to a userdevice.
 3. The method of claim 1, wherein the first biometric data andthe second biometric data comprise information captured from one or moreof a facial scanner, a camera, a microphone, a fingerprint scanner, andan iris scanner.
 4. The method of claim 1, wherein the confirmationtriggers an alarm system.
 5. The method of claim 1, wherein theconfirmation indicates the user is on a whitelist.
 6. The method ofclaim 1, wherein the comparing the first template with the secondtemplate comprises using an algorithm to classify the first template. 7.The method of claim 6, wherein the algorithm is a Support Vector Machine(SVM).
 8. The method of claim 7, wherein the algorithm is a K-NearestNeighbor (KNN) algorithm.
 9. The method of claim 1, wherein thethreshold is set by a user device.
 10. A system comprising: a processor;and a memory storing instructions thereon that, when executed by theprocessor, cause the processor to: receive a first biometric modalityand a second biometric modality associated with a user; generate a firsttemplate, the first template being a fusion of data associated with thefirst biometric modality and the second biometric modality; compare thefirst template with one or more stored templates to quantify a degree ofsimilarity therebetween; and send, when the quantified degree ofsimilarity between the first template and each of the one or more storedtemplates is below a threshold, an alert.
 11. The system of claim 10,wherein the first biometric modality and the second biometric modalityeach comprise information related to one or more of a facial scan, aniris scan, a fingerprint scan, and a voice scan.
 12. The system of claim10, wherein the alert is sent over a communication network to a userdevice.
 13. The system of claim 12, wherein the alert triggers an alarmsystem.
 14. The system of claim 10, wherein the first template isdetermined using the Neyman-Pearson lemma.
 15. The system of claim 10,wherein the degree of similarity is a percent-based value.
 16. Thesystem of claim 10, further comprising: labeling, with a respectivesession tag, each of the first biometric modality and the secondbiometric modality.
 17. The system of claim 16, wherein each of thesession tags omits associating personally identifiable information (PII)with either of the first biometric modality and the second biometricmodality.
 18. The system of claim 17, wherein a record of each of thesession tags is stored in a database.
 19. A system comprising: aprocessor; an imaging device; a user device coupled to the processor;and a memory storing instructions thereon that, when executed by theprocessor, cause the processor to: capture, using the imaging device, animage of a user; add a session tag to the captured image; generate afirst template from the captured image; determine, based on apredetermined threshold, whether the first template matches a secondtemplate; when the first template matches the second template, generateand send a confirmation to the user device; and when the first templatedoes not match the second template, generate and send an alert the userdevice.
 20. The system of claim 19, wherein the user device isconfigured to at least one of decrease and increase the predeterminedthreshold.